title: Modelling biphasic killing of fluoroquinolones: guiding optimal dosing regimen design.
abstract: OBJECTIVE
  Fluoroquinolones are commonly believed to exhibit concentration-dependent killing, but time-kill studies have revealed that fluoroquinolone activity could be a complex combination of concentration-dependent and -independent killing. We had previously developed a mathematical modelling framework to describe the dynamics of bacterial populations under the effect of antimicrobials, which could facilitate the design of optimal dosing regimens. Our objective was to extend the framework to describe the effect of fluoroquinolones on heterogeneous populations of Escherichia coli and Staphylococcus aureus.
  
  
  METHODS
  A mathematical model was fitted to time-kill data of moxifloxacin (0-128× MIC; MIC = 0.0625 mg/L) against E. coli MG1655 and levofloxacin (0-64× MIC; MIC = 0.25 mg/L) against S. aureus ATCC 29213 over 24 h. Based on the best-fit model parameters, the likelihood of resistance development associated with various dosing regimens was predicted. Subsequently, in vitro studies with a hollow-fibre infection model were selectively performed to validate the mathematical model predictions, using simulated human half-lives (moxifloxacin = 12 h; levofloxacin = 5-7 h).
  
  
  RESULTS
  Bacterial regrowth and resistance development were observed with suboptimal dosing regimens. Parallel time-growth studies substantiated the modelling assumption that there was no significant biofitness cost in resistant mutants. The mechanism of fluoroquinolone resistance was confirmed by PCR.
  
  
  CONCLUSIONS
  Our model was found to be reasonable in characterizing biphasic killing of fluoroquinolones and predicting dosing regimens to suppress resistance development. Our work demonstrated improvements resulting from using the proposed mathematical modelling as a decision support tool for guiding the design of dosing regimens.
authors.affiliation: Department of Chemical & Biomolecular Engineering, College of Engineering, University of Houston, Houston, TX 77004, USA.
authors.forename: Pratik
authors.initials: P
authors.lastname: Bhagunde
authors.forename: Renu
authors.initials: R
authors.lastname: Singh
authors.forename: Kimberly R
authors.initials: KR
authors.lastname: Ledesma
authors.forename: Kai-Tai
authors.initials: KT
authors.lastname: Chang
authors.forename: Michael
authors.initials: M
authors.lastname: Nikolaou
authors.forename: Vincent H
authors.initials: VH
authors.lastname: Tam
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001372:Aza Compounds / Q000494:pharmacology; D024881:Drug Resistance, Bacterial; D004926:Escherichia coli / Q000187:drug effects*; D024841:Fluoroquinolones / Q000494:pharmacology*; D006801:Humans; D064704:Levofloxacin; D008826:Microbial Sensitivity Tests; D050296:Microbial Viability / Q000187:drug effects*; D008954:Models, Biological; D008962:Models, Theoretical; D000077266:Moxifloxacin; D009154:Mutation; D015242:Ofloxacin / Q000494:pharmacology; D011804:Quinolines / Q000494:pharmacology; D013211:Staphylococcus aureus / Q000187:drug effects*; D013997:Time Factors
chemical_list: D000900:Anti-Bacterial Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D011804:Quinolines; D064704:Levofloxacin; D015242:Ofloxacin; D000077266:Moxifloxacin